import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_ta import TaTokenizer
else:
		__a    :	Optional[int]         =    None


__a    :	int         =    logging.get_logger(__name__)

__a    :	Union[str, Any]         =    {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__a    :	Optional[int]         =    {
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__a    :	List[Any]         =    {
    """t5-small""": 5_1_2,
    """t5-base""": 5_1_2,
    """t5-large""": 5_1_2,
    """t5-3b""": 5_1_2,
    """t5-11b""": 5_1_2,
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					List[Any]     					=		VOCAB_FILES_NAMES
			__a    :					List[Any]     					=		PRETRAINED_VOCAB_FILES_MAP
			__a    :					List[str]     					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a    :					Tuple     					=		['''input_ids''', '''attention_mask''']
			__a    :					Dict     					=		TaTokenizer

			__a    :					List[int]     					=		[]





			def __init__(  self ,  lowerCAmelCase__=None ,  lowerCAmelCase__=None ,  lowerCAmelCase__="</s>" ,  lowerCAmelCase__="<unk>" ,  lowerCAmelCase__="<pad>" ,  lowerCAmelCase__=1_00 ,  lowerCAmelCase__=None ,  **lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							if extra_ids > 0 and additional_special_tokens is None:
											__lowercase					     =				[F"<extra_id_{i}>" for i in range(lowerCAmelCase__		)]
							elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra special tokens
											__lowercase					     =				len(set(filter(lambda lowerCAmelCase__		: bool('''extra_id_''' in str(lowerCAmelCase__		)		) ,  lowerCAmelCase__		)		)		)
											if extra_tokens != extra_ids:
															raise ValueError(
															    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
															    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
															    ''' tokens'''		)

							super().__init__(
							    lowerCAmelCase__ ,  tokenizer_file=lowerCAmelCase__ ,  eos_token=lowerCAmelCase__ ,  unk_token=lowerCAmelCase__ ,  pad_token=lowerCAmelCase__ ,  extra_ids=lowerCAmelCase__ ,  additional_special_tokens=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )

							__lowercase					     =				vocab_file
							__lowercase					     =				False if not self.vocab_file else True
							__lowercase					     =				extra_ids





			@staticmethod
			def 			_SCREAMING_SNAKE_CASE     (  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Optional[Any]:

							'''simple docstring'''


							if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__lowercase					     =				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
															return init_max_model_length
											elif init_max_model_length is None:
															warnings.warn(
															    '''This tokenizer was incorrectly instantiated with a model max length of'''
															    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
															    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
															    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
															    F" {pretrained_model_name_or_path} automatically truncating your input to"
															    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
															    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
															    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
															    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.''' ,  lowerCAmelCase__ ,  )

							return max_model_length





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   Tuple[str]:

							'''simple docstring'''


							if not self.can_save_slow_tokenizer:
											raise ValueError(
											    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
											    '''tokenizer.'''		)

							if not os.path.isdir(lowerCAmelCase__		):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
											return
							__lowercase					     =				os.path.join(
							    lowerCAmelCase__ ,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

							if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCAmelCase__		):
											copyfile(self.vocab_file ,  lowerCAmelCase__		)
											logger.info(F"Copy vocab file to {out_vocab_file}"		)

							return (out_vocab_file,)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   List[int]:

							'''simple docstring'''


							__lowercase					     =				token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
							else:
											__lowercase					     =				token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ = None		)	->   List[int]:

							'''simple docstring'''


							__lowercase					     =				[self.eos_token_id]

							if token_ids_a is None:
											return len(token_ids_a + eos		) * [0]
							return len(token_ids_a + eos + token_ids_a + eos		) * [0]





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Dict:

							'''simple docstring'''


							return list(
							    set(filter(lambda lowerCAmelCase__		: bool(re.search(R'''<extra_id_\d+>''' ,  lowerCAmelCase__		)		) is not None ,  self.additional_special_tokens		)		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							return [self.convert_tokens_to_ids(lowerCAmelCase__		) for token in self.get_sentinel_tokens()]
import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.12.2"""):
		raise Exception("""requires fairseq >= 0.12.2""")
if version.parse(fairseq.__version__) > version.parse("""2"""):
		raise Exception("""requires fairseq < v2""")

logging.set_verbosity_info()
__a    :	Any         =    logging.get_logger(__name__)

__a    :	Union[str, Any]         =    """Hello, World!"""
__a    :	Optional[int]         =    """en_XX"""





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				Path('''data_bin'''				)
				__lowercase					     =				FairseqXmodModel.from_pretrained(
				    model_name_or_path=str(Path(lowercase				).parent				)				,     checkpoint_file=Path(lowercase				).name				,     _name='''xmod_base'''				,     arch='''xmod_base'''				,     task='''multilingual_masked_lm'''				,     data_name_or_path=str(lowercase				)				,     bpe='''sentencepiece'''				,     sentencepiece_model=str(Path(lowercase				).parent / '''sentencepiece.bpe.model'''				)				,     src_dict=str(data_dir / '''dict.txt'''				)				,     )
				xmod.eval()  # disable dropout
				print(lowercase				)

				__lowercase					     =				xmod.model.encoder.sentence_encoder
				__lowercase					     =				XmodConfig(
				    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings				,     hidden_size=xmod.cfg.model.encoder_embed_dim				,     num_hidden_layers=xmod.cfg.model.encoder_layers				,     num_attention_heads=xmod.cfg.model.encoder_attention_heads				,     intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim				,     max_position_embeddings=514				,     type_vocab_size=1				,     layer_norm_eps=1E-5				,     pre_norm=xmod.cfg.model.encoder_normalize_before				,     adapter_reduction_factor=getattr(xmod.cfg.model				,     '''bottleneck'''				,     2				)				,     adapter_layer_norm=xmod.cfg.model.adapter_layer_norm				,     adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm				,     ln_before_adapter=xmod.cfg.model.ln_before_adapter				,     languages=xmod.cfg.model.languages				,     )
				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

				print('''Our X-MOD config:'''				,     lowercase				)

				__lowercase					     =				XmodForSequenceClassification(lowercase				) if classification_head else XmodForMaskedLM(lowercase				)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				__lowercase					     =				xmod_sent_encoder.embed_tokens.weight
				__lowercase					     =				xmod_sent_encoder.embed_positions.weight
				__lowercase					     =				torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight				)  # just zero them out b/c xmod doesn't use them.

				__lowercase					     =				xmod_sent_encoder.layernorm_embedding.weight
				__lowercase					     =				xmod_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers				):
								# Encoder: start of layer
								__lowercase					     =				model.roberta.encoder.layer[i]
								__lowercase					     =				xmod_sent_encoder.layers[i]

								# self attention
								__lowercase					     =				layer.attention.self
								if not (
								    xmod_layer.self_attn.k_proj.weight.data.shape
								    == xmod_layer.self_attn.q_proj.weight.data.shape
								    == xmod_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)				)
								):
												raise AssertionError('''Dimensions of self-attention weights do not match.'''				)

								__lowercase					     =				xmod_layer.self_attn.q_proj.weight
								__lowercase					     =				xmod_layer.self_attn.q_proj.bias
								__lowercase					     =				xmod_layer.self_attn.k_proj.weight
								__lowercase					     =				xmod_layer.self_attn.k_proj.bias
								__lowercase					     =				xmod_layer.self_attn.v_proj.weight
								__lowercase					     =				xmod_layer.self_attn.v_proj.bias

								# self-attention output
								__lowercase					     =				layer.attention.output
								if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
												raise AssertionError('''Dimensions of self-attention output weights do not match.'''				)
								__lowercase					     =				xmod_layer.self_attn.out_proj.weight
								__lowercase					     =				xmod_layer.self_attn.out_proj.bias
								__lowercase					     =				xmod_layer.self_attn_layer_norm.weight
								__lowercase					     =				xmod_layer.self_attn_layer_norm.bias

								# intermediate
								__lowercase					     =				layer.intermediate
								if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('''Dimensions of intermediate weights do not match.'''				)
								__lowercase					     =				xmod_layer.fca.weight
								__lowercase					     =				xmod_layer.fca.bias

								# output
								__lowercase					     =				layer.output
								if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
												raise AssertionError('''Dimensions of feed-forward weights do not match.'''				)
								__lowercase					     =				xmod_layer.fca.weight
								__lowercase					     =				xmod_layer.fca.bias
								__lowercase					     =				xmod_layer.final_layer_norm.weight
								__lowercase					     =				xmod_layer.final_layer_norm.bias
								if bert_output.adapter_layer_norm is not None:
												__lowercase					     =				xmod_layer.adapter_layer_norm.weight
												__lowercase					     =				xmod_layer.adapter_layer_norm.bias

								if sorted(bert_output.adapter_modules.keys()				) != sorted(xmod_layer.adapter_modules.keys()				):
												raise AssertionError('''Lists of language adapters do not match.'''				)
								for lang_code, adapter in xmod_layer.adapter_modules.items():
												__lowercase					     =				bert_output.adapter_modules[lang_code]
												__lowercase					     =				xmod_layer.adapter_modules[lang_code]
												__lowercase					     =				from_adapter.fca.weight
												__lowercase					     =				from_adapter.fca.bias
												__lowercase					     =				from_adapter.fca.weight
												__lowercase					     =				from_adapter.fca.bias

								# end of layer

				if xmod_sent_encoder.layer_norm is not None:
								__lowercase					     =				xmod_sent_encoder.layer_norm.weight
								__lowercase					     =				xmod_sent_encoder.layer_norm.bias

				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].dense.weight
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].dense.bias
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.weight
								__lowercase					     =				xmod.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								__lowercase					     =				xmod.model.encoder.lm_head.dense.weight
								__lowercase					     =				xmod.model.encoder.lm_head.dense.bias
								__lowercase					     =				xmod.model.encoder.lm_head.layer_norm.weight
								__lowercase					     =				xmod.model.encoder.lm_head.layer_norm.bias
								__lowercase					     =				xmod.model.encoder.lm_head.weight
								__lowercase					     =				xmod.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				__lowercase					     =				xmod.encode(lowercase				).unsqueeze(0				)  # batch of size 1
				model.roberta.set_default_language(lowercase				)

				__lowercase					     =				model(lowercase				)[0]
				if classification_head:
								__lowercase					     =				xmod.model.classification_heads['''mnli'''](xmod.extract_features(lowercase				)				)
				else:
								__lowercase					     =				xmod.model(lowercase				,     lang_id=[SAMPLE_LANGUAGE]				)[0]
				print(our_output.shape				,     their_output.shape				)
				__lowercase					     =				torch.max(torch.abs(our_output - their_output				)				).item()
				print(F"max_absolute_diff = {max_absolute_diff}"				)  # ~ 1e-7
				__lowercase					     =				torch.allclose(lowercase				,     lowercase				,     atol=1E-3				)
				print('''Do both models output the same tensors?'''				,     '''🔥''' if success else '''💩'''				)
				if not success:
								raise Exception('''Something went wRoNg'''				)

				Path(lowercase				).mkdir(parents=lowercase				,     exist_ok=lowercase				)
				print(F"Saving model to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	List[str]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--xmod_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		parser.add_argument(
		    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
		)
		__a    :	List[str]         =    parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)